Bayesian inference in epidemics: linear noise analysis
This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-01-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023193?viewType=HTML |
| _version_ | 1797938811435483136 |
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| author | Samuel Bronstein Stefan Engblom Robin Marin |
| author_facet | Samuel Bronstein Stefan Engblom Robin Marin |
| author_sort | Samuel Bronstein |
| collection | DOAJ |
| description | This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of 'best case' as well as a 'worst case' analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding to a prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable. |
| first_indexed | 2024-04-10T19:05:34Z |
| format | Article |
| id | doaj.art-682c354db65b4354849ae1b5c57e9033 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-10T19:05:34Z |
| publishDate | 2023-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-682c354db65b4354849ae1b5c57e90332023-01-31T02:36:57ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-01-012024128415210.3934/mbe.2023193Bayesian inference in epidemics: linear noise analysisSamuel Bronstein 0Stefan Engblom 1Robin Marin21. Department of Mathematics and Applications, ENS Paris, 75005 Paris, France2. Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05 Uppsala, Sweden2. Division of Scientific Computing, Department of Information Technology, Uppsala University, SE-751 05 Uppsala, SwedenThis paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of 'best case' as well as a 'worst case' analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding to a prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.https://www.aimspress.com/article/doi/10.3934/mbe.2023193?viewType=HTMLparameter estimationbayesian modelingstochastic epidemiological modelsnetwork modelornstein-uhlenbeck process |
| spellingShingle | Samuel Bronstein Stefan Engblom Robin Marin Bayesian inference in epidemics: linear noise analysis Mathematical Biosciences and Engineering parameter estimation bayesian modeling stochastic epidemiological models network model ornstein-uhlenbeck process |
| title | Bayesian inference in epidemics: linear noise analysis |
| title_full | Bayesian inference in epidemics: linear noise analysis |
| title_fullStr | Bayesian inference in epidemics: linear noise analysis |
| title_full_unstemmed | Bayesian inference in epidemics: linear noise analysis |
| title_short | Bayesian inference in epidemics: linear noise analysis |
| title_sort | bayesian inference in epidemics linear noise analysis |
| topic | parameter estimation bayesian modeling stochastic epidemiological models network model ornstein-uhlenbeck process |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023193?viewType=HTML |
| work_keys_str_mv | AT samuelbronstein bayesianinferenceinepidemicslinearnoiseanalysis AT stefanengblom bayesianinferenceinepidemicslinearnoiseanalysis AT robinmarin bayesianinferenceinepidemicslinearnoiseanalysis |